Big Data Integration Research Articles

Big Data Integration Method of Mathematical Modeling and Manufacturing System Based on Fog Calculation

Using big data to promote economic development, improve social governance, and improve service and regulatory capabilities is becoming a trend. However, the current cloud computing for data processing has been difficult to meet the demand, and the server pressure has increased dramatically, so people pay special attention to the big data integration of fog computing. In order to make the application of big data meet people’s needs, we have established relevant mathematical models based on fog calculation, made system big data integration, collected relevant data, designed experiments, and obtained relevant research data by reviewing relevant literature and interviewing professionals. The research shows that big data integration using fog computing modeling has the characteristics of fast response and stable function. Compared with cloud computing and previous computer algorithms, big data integration has obvious advantages, and the computing speed is nearly 20% faster than cloud computing and about 35% higher than other computing methods. This shows that big data integration built by fog computing can have a huge impact on people’s lives.

Research on the Reform of Cost-Accounting Teaching Mode under the Background of Internet-Plus

With the continuous development and promotion of Internet technology, the deep integration of big data, cloud computing, artificial intelligence and education and teaching, the methods of classroom teaching have undergone fundamental changes, from the past single “offline classroom face-to-face instruction” method to the diversified “online and offline time and space unlimited” Internet-plus teaching model.The article aims at the traditional Cost-Accounting course teaching mode, through the aid of Internet technology, adopts the “embedded” diversified teaching mode to bring reforms to the curriculum. Through progressive and hierarchical teaching innovation, comprehensively improve students' practical skills and overall quality. Taking the Cost-Accounting course as an example, how to scientifically use the Internet-plus teaching mode to achieve the course teaching goals requires continuous practice and improvement.

Blockchain-based Big Data Integrity Service Framework for IoT Devices Data Processing in Smart Cities

Nowadays, smart cities are using advanced technologies to control and coordinate physical, social, and commercial enterprise systems to deliver high-quality services to their residents while guaranteeing the effective usage of available resources. Numerous major corporations, as well as government bodies, are involved in the construction of smart cities because smart city activities also need information that is obtained from organizations. Blockchain-based technology allows for peer-to-peer exchanges as a public ledger that maintains records of transactions, smart contracts, agreements, and shipments without third-party mediators. In the future, smart cities will be equipped with several innovative technologies, a huge amount of data, a lot of Internet of Things (IoT) devices, and a heterogeneous environment. However, data processing among IoT devices in futuristic smart cities is a challenging task. In this paper, the author proposed a blockchain-based big data integrity service framework for IoT devices data processing in smart cities. The author collected the necessary data from various resources and applied three experiments to evaluate the key performance indicators. K-mean algorithm was used to classify the data, and blockchain strategy was applied to ensure secure communication among IoT devices. The framework was simulated and tested using 100 devices. The proposed framework allowed IoT devices to efficiently use information collected from different resources to engage in operational processes and exchange information.

A novel big data approach to measure and visualize urban accessibility

Accessibility is a topic of interest to multiple disciplines for a long time. In the last decade, the increasing availability of data may have exceeded the development of accessibility modeling approaches, resulting in a modeling gap. In part, this modeling gap may have resulted from the differences needed for single versus multimodal opportunities for access to services. With a focus on large volumes of transportation data, a new measurement approach, called Urban Accessibility Relative Index (UARI), was developed for the integration of multi-mode transportation big data, including taxi, bus, and subway, to quantify, visualize and understand the spatiotemporal patterns of accessibility in urban areas. Using New York City (NYC) as the case study, this paper applies the UARI to the NYC data at a 500-m spatial resolution and an hourly temporal resolution. These high spatiotemporal resolution UARI maps enable us to measure, visualize, and compare the variability of transportation service accessibility in NYC across space and time. Results demonstrate that subways have a higher impact on public transit accessibility than bus services. Also, the UARI is greatly affected by diurnal variability of public transit service.

Sports training big data integration and optimization based on block-chain technology

The increasing maturity of Internet information technology has led to the rapid rise of block-chain technology. In essence, the block-chain is a shared database. In recent years, block-chain technology has attracted the attention of the public and society. This paper mainly conducts systematic research and analysis on the integration and optimization of big data in sports training by block-chain, and achieves large-scale consensus in the case of decentralization, therefore, the information of the network will not be tampered with and forged, thus crossing the boundary of the bottom layer of the information system. This paper is based on the integration and optimization of sports training is big data. With the help of block-chain technology, each module is designed and implemented according to the needs. At the same time, the pow algorithm inside the block-chain is improved. The research results show that it is based on the block-chain. The integration and optimization of technical sports training big data can reduce the number of sports training injuries of sports school students by 10% -20%, which is very conducive to the healthy growth and development of sports school students.

True-data testbed for 5G/B5G intelligent network

Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting internet of everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and provi di ng hi gh-quality servi ce. As a rapi d evolvi ng paradi gm, the AI-empowered mob i le communi cati ons demand large amounts of data acquired from real network environment for systematic test and verification. Hence, we build the world's first true-data testbed for 5G/B5G intelligent network (TTIN), which comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. In the TTIN, true network data acquisition, storage, standardization, and analysis are available, which enable system-level online verification of B5G/6G-orientated key technologies and support data-driven network optimization through the closed-loop control mechanism. This paper elaborates on the system architecture and module design of TTIN. Detailed technical specifications and some of the established use cases are also showcased.

Modeling the Big Data challenges in context of smart cities – an integrated fuzzy ISM-DEMATEL approach

PurposeBig Data can be utilised for efficient use of resources and to provide better services to the resident in order to enhance the delivery of urban services and create sustainable build environment. However, the adoption of Big Data faces many challenges at the implementation level. Therefore, the purpose of this paper is to identify the challenges towards the efficient application of Big Data in smart cities development and analyse the inter-relationships.Design/methodology/approachThe 14 Big Data challenges are identified through the literature review and validated with the expert’s feedback. After that the inter-relationships among the identified challenges are developed using an integrated approach of fuzzy Interpretive Structural Modelling (fuzzy-ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (fuzzy-DEMATEL).FindingsEvaluation of interrelationships among the challenges suggests that diverse population in smart cities and lack of infrastructure are the significant challenges that impede the integration of Big Data in the development of smart cities.Research limitations/implicationsThis study will enable practitioners, policy planners involved in smart city projects in tackling the challenges in an optimised manner for the hindrance free and accelerated development of smart cities.Originality/valueThis research is an initial effort to develop an interpretive structural model of Big Data challenges for smart cities development which gives a clearer picture of how the identified challenges interact with each other.

Особливості ідентифікації ризиків ринку BIG DATA

The paper hypothesizes that the dynamic digitalization of the economy, based on the benefits of using Big data, accelerates the use in management and production processes of technologies offered by this market. However, it is noted that the acquisitions of the Big data market also exacerbate the socio- economic contradictions between countries with developed market economies and institutionally underdeveloped countries, which include Ukraine. The authors of the study proposed to identify the Big data market with such key indicators as total revenue, number of Internet users, losses of Big Data market participants from data leakage. Thus, the high rates of development of the Big data market in terms of growth of total market income in 8,03 times during 2011 — 2020 were witnessed. The main segments of the Big Data market (service segment, software segment and service segment) were identified. It is established that the largest share of the Big Data market is occupied by the services segment (37,5% in 2020). From 2021, the growth rate of the software segment is expected to exceed other segments of this market — hardware and services. The results of the analysis of the Big Data market by M. Porter’s five forces model show that the most important of the competitive forces in the market is a high level of competition, in which market participants are encouraged to focus on potential needs and expectations of their customers to strengthen bases of differentiation and clear positioning of the services. According to the results of the SWOT-analysis of the Big Data market, the following strengths were identified: business expansion due to the increase in the amount of information it owns; increasing the number of customer reviews through social networks; establishing strategic partnerships with suppliers, dealers and other stakeholders through the use of Big Data; permanent training of employees to maintain the competitiveness of organizations; established IT system of the enterprise, which promotes faster adoption of effective management decisions; high incomes due to effective management decisions, possession of market research results through Big Data technologies. The identified market opportunities are: population growth, which means an increase in the number of potential consumers and the amount of data collected; growth in the number of enterprises that implement e-commerce in their activities; growth of active consumers due to the integration of Big Data into social networks; increasing the share of automated processes, which helps reduce costs; growing popularity of IT specialization in universities; globalization of the economy, which allows companies to expand their activities to other countries. On the basis of identified strengths of the market and its capabilities, the following strategic directions of development are proposed: entry of enterprises into new markets due to market globalization and effective implementation of Big Data technologies; use of social networks to collect consumer data and involve them in Big Data processes; improvement of the e-commerce system of enterprises due to the capabilities of well-established IT systems of the enterprise with the capabilities of Big Data; reduction of product prices due to cost optimization and effective interaction with contractors. The results of the research allowed to identify the following main risks of the industry: destruction of data confidentiality; collection of false data, infringement of intellectual property of a third party, etc. The formed risk matrix indicates that the most significant risks of this market are the reduction of information security of the entity due to hacker attacks (probability of 50%, significant damage) and the destruction of data confidentiality (probability of 25%, significant damage). Qualitative interpretation of risks in the Big Data market allowed to characterize the impact of adverse factors of internal and external environments, namely: insufficient unreliability of cloud storage for data storage; high level of distrust of data carriers to companies using Big Data; high staff turnover in the market, etc. Assessing the risk of data loss due to hacker attacks allowed to identify it as a risk of a high level of importance (6 p.). Based on the obtained result, it is concluded that the activities of Big Data market participants are vulnerable to possible hacker intrusions and require more effective measures to ensure reliable data protection of enterprises and their customers.

Analysis of the State of Learning in University Students with the Use of a Hadoop Framework

Currently, education is going through a critical moment due to the 2019 coronavirus disease that has been declared a pandemic. This has forced many organizations to undergo a significant transformation, rethinking key elements of their processes and the use of technology to maintain operations. The continuity of education has become dependent on technological tools, as well as on the ability of universities to cope with a precipitous transition to a remote educational model. That has generated problems that affect student learning. This work proposes the implementation of a Big Data framework to identify the factors that affect student performance and decision-making to improve learning. Similar works cover two main research topics under Big Data in education, the modeling and storage of educational data. However, they do not consider issues such as student performance and the improvement of the educational system with the integration of Big Data. In addition, this work provides a guide for future studies and highlights new insights and directions for the successful use of Big Data in education. Real-world data were collected for the evaluation of the proposed framework, the collection of these being the existing limitation in all research due to generalized rejection of data consent.

Multi-Blockchain-Based IoT Data Processing Techniques to Ensure the Integrity of IoT Data in AIoT Edge Computing Environments.

As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.

Big Data Analytics for Improving Financial Performance and Sustainability

Abstract In this study, the key drivers of sustainability commitment, green supply chain management, big data integration and green human resource practice are explored, and the impact of these sustainable capabilities on the environmental and financial performance of banks is also elaborated. In addition, the influence of green management practices on integrating big data technology into operations is presented. As for the concept of dynamic ability, it has been used to recommend and empirically test conceptual models. Data were collected through a self-administrated survey questionnaire on 317 people working in 37 banks in six Asian countries. Research suggests that big data analytics strategies have an impact on internal processes and on the stability and financial performance of banks. Besides, it is indicated that banks are committed to proper data monitoring of their customers to complete operational efficiency and sustainability goals. Furthermore, our result proved that banks practicing Green Innovation strategies experience better environmental and economic performance because their employees are already trained in Green HR. Finally, from our study, it was found that internal and external green supply chain management practices have a positive effect on the environmental and financial performance of banks, thus ensuring that the bank of Association of Southeast Asian Nations (ASEAN) mitigates the environmental impact through its operations and ultimately experiences an increase in financial performance.

Fuzzy Integration Algorithm of Big Data in Peer-to-Peer Communication Network Based on Deep Learning

There are many kinds of big data in peer to peer communication network, traditional big data integration algorithms ignore the classification of big data state. The location correlation between big data cannot be accurately obtained, which leads to the time-consuming process of big data integration and the low acquisition accuracy of big data location correlation. A fuzzy integration algorithm for big data in peer-to-peer communication network based on deep learning is proposed. According to the big data integration conditions of peer-to-peer communication network, the benchmark model of big data integration is constructed by combining semi-supervised deep learning. The marked sample updating model of the prior single classification fuzzy integrated data model is used to process the corresponding sample data. Combining the steady-state and dynamic residuals of the datum model, multiple information grains are obtained. Based on this, kalman filter is adopted to fuzzy fusion of big data to obtain state parameters, and multi-scale analysis is carried out to filter out big data noise. Using the obtained information grains to constrain the characteristic correlation degree of big data, the position correlation of big data is completed. Implement fuzzy integration algorithm design of big data in peer-to-peer communication network based on deep learning. In order to verify the effectiveness of the proposed algorithm, a simulation experiment is designed. Experimental results show that compared with many traditional methods, the proposed algorithm takes less time and the computational complexity of the proposed algorithm is significantly reduced. And the big data integration is more accurate and has better application effect.

Big Data Integration and Processing Model

The presented paper deals with data integration and sorting of Covid-19 data. The data file contains fifteen data fiels and for the design of integration and sorting model each of them is configured in data type, format and field length. For the data integration and sorting model design Talend Open Studio is used. The model concerns the performance of four main tasks: data integration, data sorting, result display, and output in .xls file format. For the sorting process two rules are assigned in accordance with the medical and biomedical requirements, namely to sort report date descending order and the Country Name field in alphabetical one

Privacy in the Era of Big Data: Unlocking the Blue Oceans of Data Paradigm in Malaysia

Big Data has revolutionized the process of online activities such as marketing and advertisement based on individual preferences in the eCommerce industry. In Malaysia, the integration of Big Data in the commercial and business environment is keenly felt by establishing the National Big Data Analytics Framework catalyzing further economic growth in all sectors. However, the distinct features of Big Data spawn issues relating to privacy, such as data profiling, lack of transparency regarding privacy policies, accidental disclosures of data, false data or false analytics results. Hence, this research provides an insight into the intersection between Big Data and an individual's fundamental rights. The trade-off between privacy breaching and preserving is becoming more intense due to the rapid advancement of Big Data. Suggesting comparative analysis method as the data analysis approach, the adequacy of the Malaysian Personal Data Protection Act 2010 (PDPA 2010) in governing the risks of Big Data is evaluated against the European Union General Data Protection Regulation (GDPR) in managing the risk arising from the integration of Big Data. This research is hoped to initiate the improvement to the legislative framework, provides fundamentals to the formulation of national policy, and creation of specific law on Big Data in Malaysia, which will subsequently benefit industrial players and stakeholders.

Eminent Data Visualization Tools for Integration of Big Data with IoT

In the 2020 era, there is an exponential increase in the number of devices and machines that are connecting with the internet to transmit information for analysis as well as increasing the rate of data generation. Big data and IoT are two new trends, combining them creates a technical revolution. Approx, ⅛ of an iceberg’s total mass is visible above water. ⅞ of its part stretches into the ocean and is hidden from our view. Similarly, we have many devices which are connected with the internet and huge amounts of data in any field is not entirely used. Now we have large amounts of dark data. Hence ,there is a need for analysis of this data and embed the big data with IoT. In this Paper. We are discussing terms about Big Data and IoT, Functioning of IoT with big data, Role of big data in IoT and Some data visualization tools for representing that data.

Research on the Application of Artificial Intelligence in Music Listening Learning -- Taking Intelligent Hearing Training as an Example

The integration of AI and intelligent big data can simulate the process of human thinking and reasoning, and realize efficient neural network evolution and algorithm iteration. Its utilization in the field of music education can extend and expand the process of music teaching, and gradually become a powerful teaching assistant and tool. Based on this, this paper first analyses the integration and utilization status of AI and music listening teaching, then studies the utilization of AI in music listening learning, and finally gives the utilization strategy and optimization strategy of AI in music listening learning.

A framework for big data integration within the strategic management process based on a balanced scorecard methodology

The purpose of this research is to study the impact of big data initiatives onstrategic management processes. While the majority of strategic management disciplines havehad research dedicated to the use of strategic management theories to understand how big dataaffect organizational performance, the body of research on big data lacks academic work capableof examining how to integrate big data into the strategic management process. The maincontributions of this work are: (1) it highlights the strategic use of big data; (2) it analyses themain frameworks/models proposed by scholars that support the use of big data as a strategicmanagement tool, and outlines this research gap; and (3) it proposes a new framework thatintegrates big data within the strategic management process based on a balanced scorecardmethodology.

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

A Proposed Integration Architecture for University Research Data Repository to Support University and University Hospital on Medical Digital Image Management and Analytics using Hadoop

Big Data has been used in university and hospital due to its enormous potential in managing large volume and many types of data. However, university that also has hospitals may need to integrate their data repository to have a single site access for easier system administration and management. The needs of image analytics for both researchers in the university and physicians in the university hospital demand the need of Big Data platform such as Hadoop framework. Based on the literatures, there are no papers that describe in detail the integration of big data for university, which include its own teaching hospital. Therefore, this paper focuses on the proposed research data architecture for university and university hospital to support data repository for both with capability of image analytics using Hadoop technology.

Big data for sustainable agri\u2010food supply chains: a review and future research perspectives

Research on agri-food supply chains (AFSCs) has attracted significant attention in recent years due to the challenges associated with sustainably feeding the global population. The purpose of this study is to review the potentials of big data for sustainable AFSCs. One hundred twenty-eight (128) journal articles were selected to identify how big data can contribute to the sustainable development of AFSCs. As part of our focus, a framework was developed based on the conceptualization of AFSCs in the extant literature to analyse big data research in the context of AFSCs and to provide insights into the potentials of the technology for agri-food businesses. The findings of the review indicate that there is a noticeable growth in the number of studies addressing the applications of big data for AFSCs. The potentials of big data for AFSC sustainability were synthesized in a summary framework, highlighting the primary resources and activities that are ready for improvement with big data. These include soil, water, crop and plant management, animal management, waste management and traceability management. The challenges of big data integration in AFSCs, the study’s implications, contributions, and the future research directions are highlighted in detail.